Human Pose Estimation Using Depth Map and Particle Swarm Optimization
Date Issued
2012
Date
2012
Author(s)
Yang, Chih-Chun
Abstract
In this thesis, we propose a human pose estimation algorithm and implement the algorithm on CUDA platform. The proposed algorithm needs only single-view depth image as input, unlike some former works which take color images or multi-view images. The proposed algorithm contains the following features: first, a 32 degree-of-free model composed of two elliptic cylinder and nine ellipsoids is adopted to formulate an optimization problem. Second, a modified particle swarm optimization (PSO) scheme is applied to solve the optimization problem. And this highly parallel algorithm is suitable to be implemented on CUDA platform to achieve real-time performance. We use the Microsoft Kinect as depth sensor and use the NVIDIA GTS450 as computing device. The experimental result shows that the proposed algorithm is robust enough to overcome the self-occlusion which is the common difficulty in this area. And with the aid of this GPU, this algorithm can work in real-time (12-33 fps).
Subjects
Human Pose Estimation
Particle Swarm Optimization
Depth Sensor
GPU Programming
Type
thesis
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